Pengfeng Li

sEMG-based continuous estimation of joint angles of human legs by using BP neural network

In this paper, we propose an mth order nonlinear model to describe the relationship between the surface electromyography (sEMG) signals and the joint angles of human legs, in which a simple BP neural network is built for the model estimation. The inputs of the model are sEMG time series that have been processed, and the outputs of the model are the joint angles of hip, knee, and ankle. To validate the effectiveness of the BP neural network, six able-bodied people and four spinal cord injury (SCI) patients participated in the experiment. Two movement modes including the treadmill exercise and the leg extension exercise at different speeds and different loads were respectively conducted by the able-bodied individuals, and only the treadmill exercise was selected for the SCI patients. Seven channels of sEMG from seven human leg muscles were recorded and three joint angles including the hip joint, knee joint and the ankle joint were sampled simultaneously. The results present that this method has a good performance on joint angles estimation by using sEMG for both able-bodied subjects and SCI patients. The average angle estimation root-mean-square (rms) error for leg extension exercise is less than 9^o, and the average rms error for treadmill exercise is less than 6^o for all the able-bodied subjects. The average angle estimation rms error of the SCI patients is even smaller (less than 5^o) than that of the able-bodied people because of a smaller movement range. This method would be used to rehabilitation robot or functional electrical stimulation (FES) for active rehabilitation of SCI patients or stroke patients based on sEMG signals.

Training strategies for a lower limb rehabilitation robot based on impedance control

This paper proposes three training strategies based on impedance control, including passive training, damping-active training and spring-active training, for a 3-DOF lower limb rehabilitation robot designed for patients with paraplegia or hemiplegia. Controllers with similar structure are developed for these training strategies, consisting of dual closed loops, the outer impedance control loop and the inner position/velocity control loop, known as position-based impedance control method. Simulation results verify that position-based impedance control approach is feasible to accomplish the training strategies.

Combined use of sEMG and accelerometer in hand motion classification considering forearm rotation

Hand motion classification using surface electromyography (sEMG) has been widely studied for its applications in upper-limb prosthesis and human-machine interface etc. Pattern-recognition based control methods have many advantages, and the reported classification accuracy can meet the requirements of practical applications. However, the pattern instability of sEMG in actual use limited their real implementations, and limb position variations may be one of the potential factors. In this paper, we give a pilot study of the reverse effect of forearm rotations on hand motion classification, and the results show that the forearm rotations can substantially degrade the classifiers performance: the average intra-position error is only 2.4%, but the average interposition classification error is as high as 44.0%. To solve this problem, we use an extra accelerometer to estimate the forearm rotation angles, and the best combination of sEMG data and accelerometer outputs can reduce the average classification error to 3.3%.

An FES cycling control system based on CPG

This paper presents a scientific strategy for cycling induced by the functional electrical stimulation. In order to simulate the FES-cycling movement produced by human body, a neuro-musculo-skeletal model containing 16 segments and 186 muscles is developed, which can simulate human movements precisely. This model contains mathematical model of electrically stimulated skeletal muscles. Having known the kinematics and dynamics of the model, we design an FES-cycling control system based on the central pattern generator (CPG), which can produce rhythm stimulus to produce desired torque and generate rhythm cycling movements. And an approach to control multiple muscles is proposed. In the end of this paper, the simulation results are provided.

An adaptive RBF neural network control strategy for lower limb rehabilitation robot

This paper proposed an adaptive control strategy based on RBF (radial basis function) neural network and PD Computed-Torque algorithmfor precise tracking of a predefined trajectory. This control strategy can not only give a small tracking error, but also have a good robustness to themodeling errors of the robot dynamics equation and also to the system friction. With this control algorithm, the robot can work in assist-as-needed mode by detecting the human active joint torque. At last, a simulation result using matlab simulink is given to illustrate the effectiveness of our control strategy.

Adaptive Neural Network Control of FES Cycling

FES cycling is a safe and easy way for the rehabilitation of spinal cord injury (SCI) patients. In order to design an control system for FES cycling, this paper presents a control strategy of the cycling induced by FES. The control system is developed based on artificial neural networks and consists of two layers: the outer layer controls the FES cycling model dynamics and generates desired torque; the inner layer controls multi-muscle to generate the torque that tracks the desired torque. And the distribution of multi-channel FES stimulation intensities is optimized based on the energy and muscle fatigue minimization principles. The simulation results show that the control system designed in this paper is stable and robust to muscle fatigue. Finally, some remarks are given on the clinical experiments of this control strategy.

Model based control of a rehabilitation robot for lower extremities

This paper mainly focuses on the trajectory tracking control of a lower extremity rehabilitation robot during passive training process of patients. Firstly, a mathematical model of the rehabilitation robot is introduced by using Lagrangian analysis. Then, a model based computed-torque control scheme is designed to control the constrained four-link robot (with patients foot fixed on robots end-effector) to track a predefined trajectory. Simulation results are provided to illustrate the effectiveness of the proposed model based computed-torque algorithm. In the simulation, a multi-body dynamics and motion software named ADAMS is used. The combined simulation of ADAMS and MATLAB is able to produce more realistic results of this complex integrated system.